philippe.massicotte@takuvik.ulaval.ca
www.pmassicotte.com
What is Dissolved Organic Matter (DOM)?
Graphic from Stedmon and Nelson (2015)
Important driver of water color in freshwater ecosystems
Its composition depend on landscape configuration
The carbon fraction (DOC) is the major part of the bio-reactive fraction of the dynamic carbon pool on Earth.
From Prairie (2008)
Important driver of ecosystem functioning:
4000+ chemical compounds, but two main groups:
| Terrestrial origin | Aquatic origin |
|---|---|
| Degradation of soil organic matter (erosion, etc.) | In situ primary production (phyto, macro) |
| Older than aquatic DOM | “Fresher” than terrestrial DOM |
| Humic and fulvic compounds | Fulvic, proteins and amino acids |
| High molecular weight (> 1 kDa) | Low molecular weight (< 1 kDa) |
| Low bio-availability for bacteria | High bio-availability for bacteria |
Many processes of transformation and production acting simultaneously.
Brownification of aquatic ecosystems
In recent decades, climate changes, eutrophication and changes in land use have contributed to increased inputs of colored terrestrial DOM in aquatic ecosystems (Roulet and Moore 2006; Haaland et al. 2010; Massicotte et al. 2013; Weyhenmeyer, Prairie, and Tranvik 2014).
Brownification of aquatic ecosystems
Project: “Relation lumière-macrophytes au LSP” (Dr. Raphael Proulx UQTR)
This has important consequences since the transformation of even a small fraction of the DOM pool can potentially have large impacts on ecosystem functioning (Prairie 2008).
Impacts of increasing of terrestrial DOM have already been documented at local and regional scales:
Increases in CO2 emissions (Lapierre et al. 2013)
Reduction in primary production due to light shading (Thrane, Hessen, and Andersen 2014; Seekell et al. 2015)
Graphic from Seekell et al. (2015)
Objective: Use all available data to explore the dynamic of DOM around the world.
Dr. Stiig Markager will be an invited Québec-Océan speaker in September 2018.
Step #1: Gather all available data containing both DOC and CDOM measurements.
Web of Science, Google Scholar as well as public data repositories were searched using terms cdom, doc, dissolved organic carbon, absorption and dissolved organic matter for datasets presenting original (i.e. not summarized) values of DOC and absorption properties of CDOM.
World map showing the spatial distribution of the observations extracted from the literature (n = 13 032 on ~70 studies conducted between 1990 and 2015).
Barplot showing the number of unique observations for (A) principal regions and (B) ecosystems.
CDOM absorption can be used to characterize the DOM pool.
Absorption of CDOM is reported at different wavelengths.
We decided to choose 350 nm has the reference wavelength because \(a_{\text{CDOM}}(350)\) was well correlated to DOC concentration.
Determination coefficient (R2) showing the goodness of the linear fit between DOC and absorption coefficients measured at different wavelengths (\(a_{\text{CDOM}}(\lambda)\)).
Step #2: estimate the value of \(a_{\text{CDOM}}(350)\)
Results of the linear regressions between \(a_{\text{CDOM}}(350)\) and \(a_{\text{CDOM}}(\lambda)\).
Strong relationship at the global scale.
(A) Global relationship between absorption at 350 nm (\(a_{\text{CDOM}}(350)\)) and dissolved organic carbon. (B) Barplot showing the determination coefficient (R2) of the linear relationships between \(a_{\text{CDOM}}(350)\) and DOC by ecosystems. The dashed horizontal line represents the average of \(R^2\).
Relationship highly variable among ecosystems.
How to characterize DOM reactivity along the aquatic continuum?
DOC-specific UV absorbance at 254 nm (\(\text{SUVA}_{254}\)) is a good proxy for estimating DOM reactivity (Weishaar et al. 2003).
\[ \text{SUVA}_{254} = \frac{a_{\text{CDOM}}(254)~[m^{-1}]}{\text{DOC}~[mg \times L^{-1}]} \]
Figure from Berggren, Laudon, and Jansson (2009)
SUVA is inversely proportional to the biodegradability of DOM: increasing SUVA values indicate a more aromatic and less biodegradable DOM pool.
Is DOM reactivity present any kind of spatial pattern? For each sample, we calculated its distance to the closest shoreline.
Positive values = inland water, negative values = ocean water
Strong bi-linear pattern along the spatial gradient (inland water \(\rightarrow\) open ocean).
Averaged SUVA254 calculated using observations from river and ocean ecosystems as a function of the distance to the closest shoreline. Positive distances represent inland samples (rivers) whereas negative distances represent oceanic samples.
Over a distance of 4000 km, a piecewise linear regression showed that the observed decrease of SUVA occurred more than 1300 times faster in freshwater ecosystems compared to marine water ecosystems, suggesting that degradation processes act preferentially CDOM over DOC.
More than just conservative mixing.
Segmentation analysis performed on the linear relationship between SUVA254 and salinity. There are at least two distinct phases of processing at low and high salinity.
How different are spectra between open ocean and freshwater?
Loiselle et al. (2009) developed an approach to characterize the absorption characteristics of CDOM. This method, based on the derivative signal of the absorption signal, allows determining the wavelength intervals where there are changes in the spectral slope.
Spectral slope curve (Sλ) calculated on averaged absorption spectra on freshwater and marine ecosystems using a 21 nm wavelength interval. R2 for the calculated fit is indicated by color for each point on the spectrum.
Collecting the dataset used in this study has shown that there are shortcomings in the CDOM community in making the scientific data openly available.
While we fully acknowledge the considerable amount of work and funds used to acquire the data, it is vital to emphasize the importance of making data available in open access databases so that the data collected can continue to contribute to progress in the field.
One of the first steps to make the data available (after a reasonable period of exclusive use) would be to use the existing data portals such as:
Pangaea (https://www.pangaea.de/)
Polar Data Catalogue (https://www.polardata.ca/)
DRYAD (http://datadryad.org/)
Nature recommended data repositories (http://www.nature.com/sdata/policies/repositories#envgeo)
Zenodo (https://zenodo.org/)
Please make your data processing and analysis reproducible!
I can’t find the data anymore…
Berggren, Martin, Hjalmar Laudon, and Mats Jansson. 2009. “Aging of allochthonous organic carbon regulates bacterial production in unproductive boreal lakes.” Limnology and Oceanography 54 (4):1333–42. https://doi.org/10.4319/lo.2009.54.4.1333.
Haaland, S., D. Hongve, H. Laudon, G. Riise, and R. D. Vogt. 2010. “Quantifying the Drivers of the Increasing Colored Organic Matter in Boreal Surface Waters.” Environmental Science & Technology 44 (8):2975–80. https://doi.org/10.1021/es903179j.
Lapierre, Jean-François, François Guillemette, Martin Berggren, and Paul a del Giorgio. 2013. “Increases in terrestrially derived carbon stimulate organic carbon processing and CO2 emissions in boreal aquatic ecosystems.” Nature Communications 4 (December):2972. https://doi.org/10.1038/ncomms3972.
Loiselle, Steven A., Luca Bracchini, Arduino M. Dattilo, Maso Ricci, Antonio Tognazzi, Andres Cézar, and Claudio Rossi. 2009. “The optical characterization of chromophoric dissolved organic matter using wavelength distribution of absorption spectral slopes.” Limnology and Oceanography 54 (2):590–97. https://doi.org/10.4319/lo.2009.54.2.0590.
Massicotte, Philippe, Denis Gratton, Jean-Jacques Frenette, and Ali A. Assani. 2013. “Spatial and temporal evolution of the St. Lawrence River spectral profile: A 25-year case study using Landsat 5 and 7 imagery.” Remote Sensing of Environment 136 (September). Elsevier B.V.:433–41. https://doi.org/10.1016/j.rse.2013.05.028.
Prairie, Yves T. 2008. “Carbocentric limnology: looking back, looking forward.” Canadian Journal of Fisheries and Aquatic Sciences 65 (3):543–48. https://doi.org/10.1139/f08-011.
Roulet, Nigel, and Tim R Moore. 2006. “Environmental chemistry: Browning the waters.” Nature 444 (7117):283–84. https://doi.org/10.1038/444283a.
Seekell, David a., Jean-François Lapierre, Jenny Ask, Ann-Kristin Bergström, Anne Deininger, Patricia Rodríguez, and Jan Karlsson. 2015. “The influence of dissolved organic carbon on primary production in northern lakes.” Limnology and Oceanography 60 (4):1276–85. https://doi.org/10.1002/lno.10096.
Stedmon, Colin A., and Norman B. Nelson. 2015. “The Optical Properties of DOM in the Ocean.” In Biogeochemistry of Marine Dissolved Organic Matter, edited by Dennis A. Hansell and Craig A. Carlson, Academic P, 481–508. Burlington: Elsevier. https://doi.org/10.1016/B978-0-12-405940-5.00010-8.
Stubbins, A., J.-F. Lapierre, M. Berggren, Y. T. Prairie, T. Dittmar, and P. A. del Giorgio. 2014. “What’s in an EEM? Molecular Signatures Associated with Dissolved Organic Fluorescence in Boreal Canada.” Environmental Science & Technology 48 (18):10598–10606. https://doi.org/10.1021/es502086e.
Thrane, Jan-Erik, Dag O. Hessen, and Tom Andersen. 2014. “The Absorption of Light in Lakes: Negative Impact of Dissolved Organic Carbon on Primary Productivity.” Ecosystems 17 (6):1040–52. https://doi.org/10.1007/s10021-014-9776-2.
Weishaar, James L, George R Aiken, Brian A Bergamaschi, Miranda S Fram, Roger Fujii, and Kenneth Mopper. 2003. “Evaluation of Specific Ultraviolet Absorbance as an Indicator of the Chemical Composition and Reactivity of Dissolved Organic Carbon.” Environmental Science & Technology 37 (20):4702–8. https://doi.org/10.1021/es030360x.
Weyhenmeyer, Gesa A., Yves T. Prairie, and Lars J. Tranvik. 2014. “Browning of Boreal Freshwaters Coupled to Carbon-Iron Interactions along the Aquatic Continuum.” Edited by Tomoya Iwata. PLoS ONE 9 (2):e88104. https://doi.org/10.1371/journal.pone.0088104.